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--- |
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base_model: silmi224/finetune-led-35000 |
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tags: |
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- summarization |
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- generated_from_trainer |
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model-index: |
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- name: led-risalah_data_v11 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# led-risalah_data_v11 |
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This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6843 |
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- Rouge1 Precision: 0.7035 |
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- Rouge1 Recall: 0.1205 |
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- Rouge1 Fmeasure: 0.2038 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | |
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|:-------------:|:------:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
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| 2.6071 | 0.9714 | 17 | 1.8938 | 0.6021 | 0.1074 | 0.1803 | |
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| 1.745 | 2.0 | 35 | 1.7661 | 0.7095 | 0.1174 | 0.1994 | |
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| 1.5717 | 2.9714 | 52 | 1.7251 | 0.6704 | 0.1176 | 0.1968 | |
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| 1.4921 | 4.0 | 70 | 1.6772 | 0.7014 | 0.1175 | 0.1986 | |
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| 1.3932 | 4.9714 | 87 | 1.6745 | 0.7008 | 0.1187 | 0.2011 | |
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| 1.3002 | 6.0 | 105 | 1.6869 | 0.6913 | 0.1196 | 0.2012 | |
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| 1.2784 | 6.9714 | 122 | 1.6857 | 0.7114 | 0.1246 | 0.2097 | |
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| 1.1779 | 7.7714 | 136 | 1.6843 | 0.7035 | 0.1205 | 0.2038 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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